Enterprise default distance determination method, device, equipment, medium and product

Abstract

The application discloses a kind of enterprise default distance determination method, device, equipment, medium and product.The enterprise default distance determination method includes: determining target industry to which target enterprise belongs;N target debt indicators corresponding to target industry are acquired, and the weight corresponding to N target debt indicators respectively, wherein the target debt indicator is the debt indicator of preset M debt indicators, and the debt indicator has preset degree influence on the default judgment of enterprise in target industry, wherein N and M are both integers greater than 1, and M≥N;The debt data corresponding to the target enterprise under N target debt indicators is acquired respectively;Based on the weight corresponding to N target debt indicators respectively, the debt data corresponding to the target enterprise under N target debt indicators is weighted and summed, and the default distance corresponding to the target enterprise is obtained.According to the embodiment of the application, the accuracy of enterprise default probability prediction result can be improved.

Description

Technical Field

[0001] This application belongs to the field of data processing, and in particular relates to a method, apparatus, equipment, medium and product for determining the distance to default for enterprises. Background Technology

[0002] The KMV (Credit Monitor Model) is a predictive model used to estimate the probability of default for borrowing companies. Its main principle is: on the debt maturity date, if the market value of the company's assets is higher than the value of the company's debt (default point), then the company's equity value is the difference between the market value of the company's assets and the value of the debt; if the company's asset value is lower than the value of the company's debt at this time, then the company sells all its assets to repay the debt, and the equity value becomes zero.

[0003] Currently, when calculating a company's default probability based on the KMV model, the process first determines the company's distance to default, and then uses the correlation between this distance and the expected default probability to determine the company's expected default probability. However, determining the distance to default often involves simply adding half the book value of the company's outstanding long-term debt to the value of its short-term debt (less than one year). This overly simplistic method of calculating the distance to default reduces the accuracy of the predicted default probability. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and product for determining the distance to corporate default, which can improve the accuracy of corporate default probability prediction results.

[0005] In a first aspect, embodiments of this application provide a method for determining the distance to corporate default, the method comprising:

[0006] Determine the target industry to which the target company belongs;

[0007] Obtain N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are the debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from the preset M debt indicators. N and M are both integers greater than 1, and M≥N.

[0008] Obtain the debt data of the target company under N target debt indicators;

[0009] Based on the weights corresponding to the N target debt indicators, the debt data of the target company under the N target debt indicators are weighted and summed to obtain the default distance corresponding to the target company.

[0010] Secondly, embodiments of this application provide a device for determining the distance to corporate default, the device comprising:

[0011] The first determination module is used to determine the target industry to which the target company belongs;

[0012] The first acquisition module is used to acquire N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are the debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from the preset M debt indicators. N and M are both integers greater than 1, and M≥N.

[0013] The second acquisition module is used to acquire the debt data of the target company under N target debt indicators;

[0014] The default distance determination module is used to perform a weighted summation of the debt data of the target company under each of the N target debt indicators, based on the weights corresponding to the N target debt indicators, to obtain the default distance to the target company.

[0015] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions;

[0016] When the processor executes the computer program instructions, it implements the steps of the enterprise default distance determination method as described in any embodiment of the first aspect.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of the enterprise default distance determination method as described in any embodiment of the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the steps of the enterprise default distance determination method as described in any embodiment of the first aspect.

[0019] The enterprise default distance determination method, apparatus, equipment, medium, and product in this application embodiment determine the target industry to which the target enterprise belongs, thereby obtaining N target debt indicators corresponding to the target industry and the weights corresponding to each of the N debt indicators. After obtaining the debt data of the target enterprise under each of the N target debt indicators, the debt data can be weighted and summed based on the weights corresponding to the N target debt indicators to obtain the default distance of the target enterprise. Therefore, in determining the default distance of the target enterprise, enterprises are classified based on industry. The debt indicators that have an impact on the enterprise default determination within the target industry to which the target enterprise belongs can be identified. The default distance of the enterprise is then calculated comprehensively using the debt data under these debt indicators. This results in a more accurate default probability prediction result based on the more accurate default distance, thus improving the accuracy of the enterprise default probability prediction result. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for determining the distance to corporate default provided in an embodiment of this application;

[0022] Figure 2 This is a flowchart illustrating the method for obtaining N target debt indicators corresponding to a target industry, as provided in an embodiment of this application.

[0023] Figure 3 This is a schematic diagram of a device for determining the distance to corporate default provided in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0027] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0028] IV (Information Value) is used to represent the degree to which a feature contributes to the prediction of a target, that is, the predictive power of the feature. Generally speaking, the higher the IV value, the stronger the predictive power of the feature and the higher the degree of information contribution.

[0029] Currently, the expected default probability of a company is predicted based on the KMV model. The KMV model utilizes the Black-Scholes option pricing formula to estimate the market value and volatility of the company's assets based on the market value and volatility of its equity, time to maturity, risk-free lending rate, and the book value of its liabilities. The distance to default is calculated based on the company's liabilities. The expected default probability is then obtained by establishing the relationship between the distance to default and the expected default frequency (EDF).

[0030] In related technologies, determining a company's distance to default often involves simply adding half the book value of its short-term debt (less than one year) to its outstanding long-term debt. This overly simplistic method of calculating the distance to default reduces the accuracy of predicting the probability of corporate default.

[0031] To address the problems in the prior art, embodiments of this application provide a method, apparatus, equipment, medium, and product for determining the distance to corporate default.

[0032] The method for determining the distance to corporate default provided in this application will be described in detail below with reference to the accompanying drawings, through specific embodiments and application scenarios.

[0033] Figure 1This is a flowchart illustrating a method for determining the distance to corporate default provided in an embodiment of this application. Figure 1 As shown, the method for determining the distance to default for this enterprise may specifically include the following steps:

[0034] S110. Determine the target industry to which the target company belongs;

[0035] S120. Obtain N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are the debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from the preset M debt indicators. N and M are both integers greater than 1, and M≥N.

[0036] S130. Obtain the debt data of the target company under each of the N target debt indicators;

[0037] S140. Based on the weights corresponding to the N target debt indicators, the debt data of the target company under the N target debt indicators are weighted and summed to obtain the default distance corresponding to the target company.

[0038] Therefore, by determining the target industry to which the target company belongs, N target debt indicators corresponding to the target industry and their respective weights are obtained. After obtaining the debt data of the target company under each of the N target debt indicators, the debt data can be weighted and summed based on the weights of each of the N target debt indicators to obtain the default distance of the target company. Thus, in determining the default distance of the target company, the company is classified based on its industry. Based on the target industry to which the target company belongs, the debt indicators that have an impact on the judgment of corporate default within that industry can be identified. By comprehensively calculating the company's default distance using the debt data under these debt indicators, the default probability prediction result determined based on the more accurate default distance will be more accurate, thereby improving the accuracy of the corporate default probability prediction result.

[0039] The specific implementation methods for each of the above steps are described below.

[0040] In some implementations, in S110, the target industry can correspond to multiple debt indicators, and different industries can correspond to different debt indicators. Debt can be mainly divided into three categories: long-term debt raised for strategic development needs, short-term loans borrowed to address short-term funding shortages, and accounts payable arising from daily operations. Medium- and long-term debt is mainly proactively undertaken debt, which can be repaid with the profits from the strategic plan. Short-term debt is mainly used for short-term working capital needs. Debt indicators may include, for example, short-term loans, long-term loans, accounts payable, employee compensation payable, taxes payable, and other payables such as amounts owed by the company to individuals.

[0041] In some implementations, in S120, the preset M debt indicators can be multiple debt indicators corresponding to the target industry. N target debt indicators can be determined based on the M indicators, i.e., debt indicators that have a preset influence on the determination of corporate default in the target industry. The preset influence here can be, for example, that the information value corresponding to the debt indicator reaches a certain level.

[0042] In some embodiments, such as Figure 2 As shown, in order to obtain N target debt indicators corresponding to the target industry, the above-mentioned method of obtaining N target debt indicators corresponding to the target industry may specifically include steps S210-S240.

[0043] S210. Obtain companies belonging to the target industry from multiple companies to obtain the target company set.

[0044] In some embodiments, after determining the target industry to which the target company belongs, multiple companies belonging to the target industry can be obtained to obtain a target company set.

[0045] S220. Classify the enterprises in the target enterprise set according to whether the enterprises have defaulted or gone bankrupt, and obtain the classification results. The classification results include a first category of enterprises and a second category of enterprises. The first category of enterprises includes enterprises that have defaulted or gone bankrupt, and the second category of enterprises includes enterprises that have neither defaulted nor gone bankrupt.

[0046] In some embodiments, enterprise information can be queried from the business registration information platform, and enterprise debt data can be obtained. This allows for the acquisition of default or bankruptcy information for enterprises within the target enterprise set. Based on the acquired enterprise information, enterprises that have defaulted or gone bankrupt can be classified into Category I enterprises, and enterprises that have neither defaulted nor gone bankrupt can be classified into Category II enterprises.

[0047] S230. Determine the information value corresponding to each of the M debt indicators based on the classification results.

[0048] In some embodiments, the information value corresponding to each of the M debt indicators can be determined, for example, by data binning.

[0049] As an example, in order to determine the information value corresponding to each of the M debt indicators, the above S230 may specifically include:

[0050] For each of the M debt indicators, perform the following steps to obtain the information value corresponding to each of the M debt indicators:

[0051] Obtain the debt data for each of the M debt indicators for each company in the target company set;

[0052] The debt data under each debt indicator is binned separately to obtain multiple data bins under each debt indicator;

[0053] Based on multiple data bins under each debt indicator and the classification results, the information value corresponding to each debt indicator is calculated.

[0054] In some embodiments, binning can be determined based on the specific circumstances of the debt indicators. For example, binning based on the size of the debt data can result in multiple debt data segments of different sizes, with each debt data segment being a data bin.

[0055] In some embodiments, the calculation of the information value corresponding to each debt indicator based on multiple data bins under each debt indicator and the classification results may specifically include:

[0056] Based on the classification results, determine the number of first-class enterprises and second-class enterprises in the target enterprise set, as well as the number of first-class enterprises and second-class enterprises corresponding to each data bin in multiple data bins;

[0057] The information value corresponding to each debt indicator is determined based on the number of first-category and second-category enterprises in the target enterprise set, as well as the number of first-category and second-category enterprises in each data bin across multiple data bins.

[0058] As an example, after obtaining the debt data for each company in the target company set under a certain debt indicator, the debt data for each company in the target company set under M debt indicators can be binned according to the size of the debt data. Based on the data binning, multiple debt data segments of different sizes can be obtained. For each debt data segment, the number of companies in the first category and the number of companies in the second category within that debt data segment can be counted. Thus, based on the number of companies in the first category corresponding to the i-th data bin, Bad... i The number of second-class enterprises Good i And the number of first-type enterprises in the target enterprise set (Bad) T The number of second-class enterprises Good T According to the following formula (1), the information value IV corresponding to the debt indicator in the i-th data bin can be obtained. i Then, according to the following formula (2), the sum of the information values ​​corresponding to the debt indicator in each data bin can be determined, that is, the information value IV corresponding to the debt indicator.

[0059]

[0060]

[0061] Where n represents the total number of data bins under a certain debt indicator, WOE i This represents the Weight of Evidence (WOE) value corresponding to the i-th data bin under this debt indicator. i Specifically, it can be determined according to the following formula (3).

[0062]

[0063] S240. Determine the debt indicators with information value greater than a preset threshold from among the M debt indicators as target debt indicators, and obtain N target debt indicators corresponding to the target industry.

[0064] In some embodiments, the preset threshold may be, for example, the information value of a debt indicator being greater than 0.2.

[0065] As an example, based on the information value of the M debt indicators obtained, the debt indicators with an information value greater than 0.2 are identified as target debt indicators, thus obtaining N target debt indicators corresponding to the target industry.

[0066] Therefore, by classifying the acquired set of target companies according to whether they have defaulted or gone bankrupt, and based on the data binning and classification results under each debt indicator, we can obtain the information value corresponding to M debt indicators. Furthermore, based on preset thresholds, we can obtain N target debt indicators, classify corporate debt, and subsequently obtain a more accurate distance to corporate default.

[0067] In some embodiments, in S130, after determining N target debt indicators based on information value, the debt data corresponding to the target enterprise under each of the N target debt indicators can be obtained.

[0068] In some embodiments, in S140, the weights corresponding to the N target debt indicators can be obtained based on the established logistic regression model. Finally, by weighting and summing the debt data according to the weights corresponding to the N target debt indicators, the default distance of the target enterprise can be obtained.

[0069] In some embodiments, to determine the weights corresponding to the N target debts, obtaining the weights corresponding to the N target debts may specifically include:

[0070] Obtain the debt data for each of the N target debt indicators for each enterprise in the target enterprise set;

[0071] Based on debt data and classification results, a logistic regression model is constructed;

[0072] Based on the logistic regression model, determine the weights corresponding to the N target debt indicators.

[0073] In one embodiment, the logistic regression model can be constructed based on the debt data of multiple enterprises under each target debt indicator and the classification of each enterprise. For example, the debt data of each enterprise under N target debt indicators and the type label value of the enterprise can be substituted into the model to fit the data between different enterprises to construct the logistic regression model.

[0074] As an example, the logistic regression model constructed above based on debt data and classification results can specifically include:

[0075] Based on the classification results, determine the label value corresponding to each enterprise in the target enterprise set, where the label value of the first type of enterprise is the first value, and the label value of the second type of enterprise is the second value;

[0076] We construct a logistic regression model by using the debt data of each enterprise in the target enterprise set under each of the N target debt indicators as independent variables and the corresponding label value of the enterprise as the dependent variable.

[0077] In some embodiments, the tag value for the first type of enterprise may be, for example, 1, and the tag value for the second type of enterprise may be, for example, 0.

[0078] As an example, after determining the label value corresponding to each enterprise in the target enterprise set, the debt data of the i-th enterprise in the target enterprise set under N target debt indicators can be used as the independent variable X. i And the label value corresponding to the i-th enterprise is used as the dependent variable Y. i The logistic regression model is constructed according to the following formula (4).

[0079]

[0080] Among them, X i =(1,x i1 ,x i2 ,…,x ij Let β = (β0, β1, ..., β2) represent the set of debt data corresponding to the i-th enterprise in the target enterprise set under N target debt indicators. j ) represents the set of weights corresponding to N target debt indicators, where N = j + 1, Y i This represents the label value corresponding to the i-th company in the target company set. Thus, based on the logistic regression model, the weight set β of the N target debt indicators can be determined, and this weight set β can include the weight corresponding to each target debt indicator.

[0081] Therefore, the weights corresponding to the N target debt indicators can be determined by the logistic regression model. By weighted summing of the debt data of the target company under the N target debt indicators based on the weights, the accurate default distance corresponding to the target company can be obtained. By the correspondence between the default distance of the company and the expected default probability, a more accurate expected default probability of the company can be obtained, thus improving the prediction accuracy of the company's default probability.

[0082] It should be noted that the application scenarios described in the above embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0083] Based on the same inventive concept, this application also provides a device for determining the distance to corporate default. Specifically, in conjunction with... Figure 3 Please provide a detailed explanation.

[0084] Figure 3 This is a schematic diagram of a device for determining the distance to corporate default provided in an embodiment of this application.

[0085] like Figure 3 As shown, the enterprise default distance determination device 300 may include:

[0086] The first determining module 301 is used to determine the target industry to which the target company belongs;

[0087] The first acquisition module 302 is used to acquire N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are the debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from the preset M debt indicators. N and M are both integers greater than 1, and M≥N.

[0088] The second acquisition module 303 is used to acquire the debt data of the target company under N target debt indicators respectively;

[0089] The default distance determination module 304 is used to perform weighted summation of the debt data of the target enterprise under the N target debt indicators based on the weights corresponding to the N target debt indicators, so as to obtain the default distance corresponding to the target enterprise.

[0090] The above-mentioned enterprise default distance determination device 300 is described in detail below:

[0091] In some embodiments, in order to obtain N target debt indicators corresponding to the target industry, the first acquisition module 302 mentioned above may specifically include:

[0092] The acquisition submodule is used to extract companies belonging to the target industry from multiple companies, thus obtaining a set of target companies;

[0093] The classification submodule is used to classify the enterprises in the target enterprise set according to whether the enterprises have defaulted or gone bankrupt, and obtain the classification results. The classification results include a first category of enterprises and a second category of enterprises. The first category of enterprises includes enterprises that have defaulted or gone bankrupt, and the second category of enterprises includes enterprises that have neither defaulted nor gone bankrupt.

[0094] The first determination submodule is used to determine the information value corresponding to each of the M debt indicators based on the classification results;

[0095] The second determination submodule is used to identify the debt indicators with information value greater than a preset threshold among the M debt indicators as target debt indicators, thereby obtaining N target debt indicators corresponding to the target industry.

[0096] In some embodiments, in order to determine the information value corresponding to each of the M debt indicators, the aforementioned first determining submodule may specifically include:

[0097] The acquisition unit is used to acquire the debt data of each enterprise in the target enterprise set for each of the M debt indicators;

[0098] The binning processing unit performs binning processing on the debt data under each debt indicator, resulting in multiple data bins under each debt indicator.

[0099] The calculation unit is used to calculate the information value corresponding to each debt indicator based on the results of multiple data binning and classification under each debt indicator.

[0100] In some embodiments, the above-mentioned computing unit may specifically include:

[0101] The first determining unit is used to determine, based on the classification results, the number of first-class enterprises and the number of second-class enterprises in the target enterprise set, as well as the number of first-class enterprises and the number of second-class enterprises corresponding to each data bin in the multiple data bins;

[0102] The second determining unit is used to determine the information value corresponding to each debt indicator based on the number of first-class enterprises and second-class enterprises in the target enterprise set, as well as the number of first-class enterprises and second-class enterprises corresponding to each data bin in multiple data bins.

[0103] In some embodiments, in order to determine the weights corresponding to the N target debts, the aforementioned corporate default distance determination device 300 may specifically include:

[0104] The third acquisition module is used to acquire the debt data of each enterprise in the target enterprise set under each of the N target debt indicators;

[0105] The building module is used to construct a logistic regression model based on debt data and classification results;

[0106] The second determination module is used to determine the weights corresponding to the N target debt indicators based on the logistic regression model.

[0107] In some embodiments, the above-mentioned building module may specifically include:

[0108] The third determination submodule is used to determine the tag value corresponding to each enterprise in the target enterprise set, wherein the tag value of the first type of enterprise is the first value, and the tag value of the second type of enterprise is the second value;

[0109] A submodule is constructed to use the debt data of each enterprise in the target enterprise set under each of the N target debt indicators as independent variables and the corresponding label value of the enterprise as dependent variable to construct a logistic regression model.

[0110] Therefore, by determining the target industry to which the target company belongs, N target debt indicators corresponding to the target industry and their respective weights are obtained. After obtaining the debt data of the target company under each of the N target debt indicators, the debt data can be weighted and summed based on the weights of each of the N target debt indicators to obtain the default distance of the target company. Thus, in determining the default distance of the target company, the company is classified based on its industry. Based on the target industry to which the target company belongs, the debt indicators that have an impact on the judgment of corporate default within that industry can be identified. By comprehensively calculating the company's default distance using the debt data under these debt indicators, the default probability prediction result determined based on the more accurate default distance will be more accurate, thereby improving the accuracy of the corporate default probability prediction result.

[0111] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0112] The electronic device 400 may include a processor 401 and a memory 402 storing computer program instructions.

[0113] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0114] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0115] In certain embodiments, the memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Thus, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this application.

[0116] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the enterprise default distance determination methods in the above embodiments.

[0117] In some examples, electronic device 400 may also include communication interface 403 and bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0118] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0119] Bus 410 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not as a limitation, bus 410 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0120] For example, the electronic device 400 can be a mobile phone, tablet computer, laptop computer, handheld computer, in-vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc.

[0121] The electronic device 400 can execute the enterprise default distance determination method in the embodiments of this application, thereby achieving a combination Figure 1 and Figure 3 The method and apparatus described are for determining the distance to corporate default.

[0122] Furthermore, in conjunction with the enterprise default distance determination method in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the enterprise default distance determination methods in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0123] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0124] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0125] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0126] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0127] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining the distance to corporate default, characterized in that, include: Determine the target industry to which the target company belongs; Obtain N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from M preset debt indicators. N and M are both integers greater than 1, and M≥N. Different industries correspond to different debt indicators. The debt indicators include at least one of short-term loans, long-term loans, accounts payable, employee compensation payable, taxes payable, and amounts owed by enterprises to individuals. Obtain the debt data of the target enterprise under each of the N target debt indicators; Based on the weights corresponding to the N target debt indicators, the debt data of the target company under the N target debt indicators are weighted and summed to obtain the default distance corresponding to the target company. The acquisition of N target debt indicators corresponding to the target industry includes: From multiple enterprises, obtain enterprises belonging to the target industry to obtain the target enterprise set; The companies included in the target company set are classified according to whether they have defaulted or gone bankrupt, resulting in a classification result. The classification result includes a first category of companies and a second category of companies. The first category of companies includes companies that have defaulted or gone bankrupt, and the second category of companies includes companies that have neither defaulted nor gone bankrupt. Based on the classification results, determine the information value corresponding to each of the M debt indicators; Among the M debt indicators, those with information value greater than a preset threshold are identified as target debt indicators, thus obtaining the N target debt indicators corresponding to the target industry.

2. The method according to claim 1, characterized in that, The determination of the information value corresponding to each of the M debt indicators based on the classification results includes: For each of the M debt indicators, perform the following steps to obtain the information value corresponding to each of the M debt indicators: Obtain the debt data for each enterprise in the target enterprise set under each of the M debt indicators; The debt data under each debt indicator is binned separately to obtain multiple data bins under each debt indicator; Based on multiple data bins under each debt indicator and the classification results, the information value corresponding to each debt indicator is calculated.

3. The method according to claim 2, characterized in that, The calculation of the information value corresponding to each debt indicator based on multiple data bins under each debt indicator and the classification results includes: Based on the classification results, the number of enterprises of the first type and the number of enterprises of the second type in the target enterprise set are determined, as well as the number of enterprises of the first type and the number of enterprises of the second type corresponding to each data bin in the plurality of data bins; Based on the number of the first type of enterprises and the number of the second type of enterprises in the target enterprise set, and the number of the first type of enterprises and the number of the second type of enterprises corresponding to each data bin in the plurality of data bins, the information value corresponding to each debt indicator is determined.

4. The method according to claim 2, characterized in that, Obtain the weights corresponding to the N target debt indicators, including: Obtain the debt data for each enterprise in the target enterprise set under each of the N target debt indicators; Based on the debt data and the classification results, a logistic regression model is constructed; Based on the logistic regression model, determine the weights corresponding to the N target debt indicators.

5. The method according to claim 4, characterized in that, The step of constructing a logistic regression model based on the debt data and the classification results includes: Based on the classification results, a label value is determined for each enterprise in the target enterprise set, wherein the label value of the first type of enterprise is a first value, and the label value of the second type of enterprise is a second value. A logistic regression model is constructed by taking the debt data corresponding to each of the N target debt indicators for each enterprise in the target enterprise set as the independent variable and the corresponding label value of the enterprise as the dependent variable.

6. A device for determining the distance to corporate default, characterized in that, The device includes: The determination module is used to determine the target industry to which the target company belongs; The first acquisition module is used to acquire N target debt indicators corresponding to the target industry, and the weights corresponding to the N target debt indicators respectively. The target debt indicators are debt indicators that have a preset degree of influence on the judgment of corporate default in the target industry from a preset M debt indicators. N and M are both integers greater than 1, and M≥N. Different industries correspond to different debt indicators. The debt indicators include at least one of short-term loans, long-term loans, accounts payable, employee compensation payable, taxes payable, and amounts owed by enterprises to individuals. The second acquisition module is used to acquire the debt data of the target enterprise under the N target debt indicators respectively; The default distance determination module is used to perform a weighted summation of the debt data of the target enterprise under the N target debt indicators based on the weights corresponding to the N target debt indicators, so as to obtain the default distance corresponding to the target enterprise. The first acquisition module is used to acquire enterprises belonging to the target industry from multiple enterprises to obtain a target enterprise set; classify the enterprises in the target enterprise set according to whether the enterprises have defaulted or gone bankrupt to obtain a classification result, wherein the classification result includes a first category of enterprises and a second category of enterprises, wherein the first category of enterprises includes enterprises that have defaulted or gone bankrupt, and the second category of enterprises includes enterprises that have neither defaulted nor gone bankrupt; determine the information value corresponding to the M debt indicators based on the classification result; and determine the debt indicators among the M debt indicators whose information value is greater than a preset threshold as target debt indicators to obtain the N target debt indicators corresponding to the target industry.

7. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the steps of the enterprise default distance determination method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the steps of the enterprise default distance determination method as described in any one of claims 1-5.

9. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device causes the electronic device to perform the steps of the enterprise default distance determination method as described in any one of claims 1-5.

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